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Striking the balance in data governance

16 September 2010

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Despite recent news reports that the Basel III liquidity requirements are to be pared back, financial institutions are still facing vast regulatory challenges. They already have to hold more capital and liquidity, as well as report on this daily. Going forward,
institutions will also have to demonstrate the processes and controls used to put together reliable information. Never before have risk and data management been so interwoven.

The real challenges facing institutions looking to showcase their transparency to regulators are the problems that result from siloed business units and manual processes. These can result in institutions not being able to determine a good instrument price
from a bad one, or even tell who issued the instrument in the first place. If they can’t do that then they certainly can’t demonstrate independent pricing. Furthermore, data management often falls under the responsibility of IT. However, all too often the
IT department doesn’t have the specific front and middle office insight needed to ensure that data is managed according to business requirements.

Let’s not forget what’s at stake if data governance fails – sure, the fines can be large, but it’s the increased capital margins entailed in not being able to demonstrate compliance that really hurt. Also, throwing money at the issue and massively boosting
compliance costs is not necessarily an option at a time when budgets are so severely limited.

So what’s the solution? Institutions need to take a business process-led approach to data governance, based on the principles of independence and transparency. Incorporating business rules into data management is a must. These rules or service level agreements
will be specific to the bank – such as guaranteed delivery times of prices from specific countries. Right now, most banks haven’t yet joined this up to the data management process, which is what makes it so difficult to determine what went wrong or gain an
early warning of potential issues. As a result, what could have been addressed as a small discrepancy often snowballs into a big problem.

Equally important is being able to draw up a full audit trail of the data. This should not only show exactly where the data has come from, but must also provide real-time information about all data events, such as errors, new product take-ons and model updates.
Continuous monitoring for data governance of the whole, and preferably centralised, process is also paramount. In taking this measure, banks will achieve better compliance and drive down operational risk by giving business users unparalleled transparency.

These best practices – particularly if conducted across price, reference, counterparty and corporate actions data – are key to mastering the compliance and competitive balancing act. If institutions act now and get this right, they will not only comply with
these increasingly stringent regulations, but also reduce their risk and capital overhead.